Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Machine learning-based prediction of residual flexural strength in fiber-reinforced ultra-high-performance concrete under elevated temperatures
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 23 March 2026

Machine learning-based prediction of residual flexural strength in fiber-reinforced ultra-high-performance concrete under elevated temperatures

  • Manish Kewalramani1,
  • Arsalan Mahmoodzadeh2,
  • Mohamed Hechmi El Ouni3,4,
  • Abdulaziz Alghamdi5,
  • Anwar Ahmed6,
  • Ibrahim Albaijan7,
  • Sivaprakasam Palani8 &
  • …
  • Raouf Hassan9 

Scientific Reports , Article number:  (2026) Cite this article

  • 490 Accesses

  • 1 Altmetric

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Materials science

Abstract

Fiber-reinforced ultra-high-performance concrete (FR-UHPC) has matured over the past 30 years from niche lab mixes to critical responses for high-performance infrastructure, but capabilities of the community continue lag behind expressed multi-crosscut needs in robust vascular prediction of mechanical performance after exposure to fire across modern concrete families. Extension from the historical empirical and mechanistic modeling to more recent data-driven endeavours along with contributions herein, this study presents and validates a full machine learning-based, interpretable framework for the residual flexural strength (RFS) of FR-UHPC following exposure to elevated temperatures. Utilizing a wide experimental database of 800 three-point bending tests, covering binder chemistry, contents of silica-fume and fly-ash, various fiber types (PVA, steel, basalt), curing regimes, and thermal paths, nine state-of-the-art algorithms including the novel hybrid AegisFusion architecture were benchmarked. AegisFusion is a dual-track neural/tree fusion with MetaSwarm tuning and Bayesian calibration. It achieved superior accuracy and calibrated uncertainty in both hold-out (R² = 0.98, RMSE ≈ 0.52 MPa, VAF = 0.98), and 5-fold (R² = 0.93–0.98; RMSE = 0.28‒0.57, VAF = 0.93‒0.98) testing phases. Rigorous non-parametric testing including Friedman and Nemenyi critical difference analysis, ranked AegisFusion best. Model explainability via SHAP and distance-correlation analysis identified exposure maximum temperature (EMT) and fiber volume content (FV) as dominant drivers of RFS, revealing thresholded and nonlinear interactions that align with known thermo-mechanical degradation mechanisms. This study thus forges a data-centric bridge between the historical experimental foundations of FR-UHPC research and contemporary, uncertainty-aware artificial intelligence tools for resilient infrastructure design.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to restrictions imposed by research sponsors, ongoing analysis for future studies, and the necessity to maintain data confidentiality until further validation and publication, but are available from Dr. Arsalan Mahmoodzadeh on reasonable request.

Abbreviations

R2:

Coefficient of determination

RMSE:

Root mean squared Error

MAPE:

Mean absolute percentage error

VAF:

Variance accounted For

SHAP:

SHapley additive explanations value

CT:

Curing time

EMT:

Exposure maximum temperature

w/c:

Water-to-binder ratio

FVC:

Fiber volume content

SFC:

Silica fume content

FAC:

Fly-ash content

SC:

Superplasticizer content

FT:

Fiber type

AT:

Aggregate type

RFS:

Residual flexural strength

UHPC:

Ultra-high performance concrete

FR-UHPC:

Fiber-reinforced ultra-high performance concrete

FRC:

Fiber-reinforced concrete

ANN:

Artificial neural networks

SVR:

Support vector regression

RFR:

Random forest regression

XGBR:

Extreme gradient boosting regression

NuSVR:

Nu-support vector regression

GPR:

Gaussian process regression

GBR:

Gradient boosted regression

DTR:

Decision tree regression

References

  1. Abbas, S., Nehdi, M. L. & Saleem, M. A. Ultra-High Performance Concrete: Mechanical Performance, Durability, Sustainability and Implementation Challenges. Int. J. Concrete Struct. Mater. 10 (3), 271–295. https://doi.org/10.1007/s40069-016-0157-4 (2016).

    Google Scholar 

  2. Azmee, N. M. & Shafiq, N. Ultra-high performance concrete: From fundamental to applications. Case Stud. Constr. Mater. 9, e00197. https://doi.org/10.1016/j.cscm.2018.e00197 (2018).

    Google Scholar 

  3. Bajaber, M. A. & Hakeem, I. Y. UHPC evolution, development, and utilization in construction: a review. J. Mater. Res. Technol. 10, 1058–1074. https://doi.org/10.1016/j.jmrt.2020.12.051 (2021).

    Google Scholar 

  4. Escalante-Tovar, J. D., Abellán-García, J. & Fernández-Gómez, J. Predicting and Unraveling Flexural Behavior in Fiber-Reinforced UHPC Through Based Machine Learning Models. J. Compos. Sci. 9 (7), 333. https://doi.org/10.3390/jcs9070333 (2025).

    Google Scholar 

  5. Zhang, Y., Wang, Z., Xi, M., Zhao, Y. & Liu, J. Data-driven prediction of residual flexural capacity in corroded RC beams using PSO and GA-optimized CatBoost ensemble models. Eng. Res. Express. 7 (3), 035129. https://doi.org/10.1088/2631-8695/adfcb3 (2025).

    Google Scholar 

  6. Xu, Q. et al. Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns. Mater. Today Commun. 41, 110999. https://doi.org/10.1016/j.mtcomm.2024.110999 (2024).

    Google Scholar 

  7. Zhang, Y., Xi, M., Liu, J., Wang, W. & Lyu, X. Axial compressive capacity prediction of coal gangue concrete-filled steel tube stub columns: Genetic programming-augmented code calibration and Bayesian-optimized machine learning. Structures 80, 110119. https://doi.org/10.1016/j.istruc.2025.110119 (2025).

    Google Scholar 

  8. Meng, W. & Khayat, K. H. Effect of Hybrid Fibers on Fresh Properties, Mechanical Properties, and Autogenous Shrinkage of Cost-Effective UHPC. J. Mater. Civ. Eng. 30 (4). https://doi.org/10.1061/(ASCE)MT.1943-5533.0002212 (2018).

  9. Wu, Z., Shi, C., He, W. & Wu, L. Effects of steel fiber content and shape on mechanical properties of ultra high performance concrete. Constr. Build. Mater. 103, 8–14. https://doi.org/10.1016/j.conbuildmat.2015.11.028 (2016).

    Google Scholar 

  10. Nasiri, H., Pourbaba, M. & Lotfollahi Yaghin, M. A. Numerical study on the flexural and shear behavior of steel fiber and high-strength steel combination in ultra‐high‐performance fiber‐reinforced concrete beams under cyclic loading. Struct. Concrete. 26 (3), 3663–3677. https://doi.org/10.1002/suco.202300760 (2025).

    Google Scholar 

  11. Nasrin, S. & Ibrahim, A. Flexural response of Ultra-High-Performance Concrete (UHPC) hybrid bridge deck connections made with local materials. Constr. Build. Mater. 270 (121451). https://doi.org/10.1016/j.conbuildmat.2020.121451 (2021).

  12. Jiao, Y. et al. Mechanical and fracture properties of ultra-high performance concrete (UHPC) containing waste glass sand as partial replacement material. J. Clean. Prod. 277 (123501). https://doi.org/10.1016/j.jclepro.2020.123501 (2020).

  13. Abellán-García, J., Carvajal-Muñoz, J. S. & Ramírez-Munévar, C. Application of ultra-high-performance concrete as bridge pavement overlays: Literature review and case studies. Constr. Build. Mater. 410 (134221). https://doi.org/10.1016/j.conbuildmat.2023.134221 (2024).

  14. Tayeh, B. A., Askar, L. K., Askar, M. & Bakar, B. H. A. Ultra-High-Performance Concrete (UHPC) - Applications Worldwide: A State-of-the-Art Review. J. Eng. Res. Technol. 10 (1). https://doi.org/10.33976/JERT.10.1/2023/2 (2023).

  15. Perry, V. H. Ultra-High-Performance-Concrete Advancements and Industrialization—The Need for Standard Testing. Adv. Civil Eng. Mater. 4 (2), 1–16. https://doi.org/10.1520/ACEM20140028 (2015).

    Google Scholar 

  16. Zhang, K. The Characteristics of Ultra-High Performance Concrete and Its Application in Structures. Highlights Sci. Eng. Technol. 106, 513–518. https://doi.org/10.54097/81ydbq69 (2024).

    Google Scholar 

  17. Teng, L. & Khayat, K. H. Effect of overlay thickness, fiber volume, and shrinkage mitigation on flexural behavior of thin bonded ultra-high-performance concrete overlay slab. Cem. Concr. Compos. 134 (104752). https://doi.org/10.1016/j.cemconcomp.2022.104752 (2022).

  18. Hiew, S. Y., Bin Teoh, K., Raman, S. N., Kong, D. & Hafezolghorani, M. A generalised predictive model for the mechanical properties of mono/hybrid fibre-reinforced ultra-high-performance concrete. Constr. Build. Mater. 426 (136154). https://doi.org/10.1016/j.conbuildmat.2024.136154 (2024).

  19. Xu, Q. et al. An explainable ensemble learning framework for flexible pavement roughness prediction under multi-climate stressors. Case Stud. Constr. Mater. 23 (e05402). https://doi.org/10.1016/j.cscm.2025.e05402 (2025).

  20. Zhang, Y. et al. A metaheuristic-driven categorical boosting framework with interpretability for high-precision prediction of mechanical properties in corroded reinforced concrete beams. Eng. Appl. Artif. Intell. 163, 112804. https://doi.org/10.1016/j.engappai.2025.112804 (2026).

    Google Scholar 

  21. Zhang, J. & Zhao, Y. Prediction of Compressive Strength of Ultra-High Performance Concrete (UHPC) Containing Supplementary Cementitious Materials, in International Conference on Smart Grid and Electrical Automation (ICSGEA), May 2017, 522–525., May 2017, 522–525. (2017). https://doi.org/10.1109/ICSGEA.2017.150

  22. Marani, A., Jamali, A. & Nehdi, M. L. Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks. Materials 13 (21), 4757. https://doi.org/10.3390/ma13214757 (2020).

    Google Scholar 

  23. Alabduljabbar, H. et al. Predicting ultra-high-performance concrete compressive strength using gene expression programming method. Case Stud. Constr. Mater. 18, e02074. https://doi.org/10.1016/j.cscm.2023.e02074 (2023).

    Google Scholar 

  24. Ergen, F. & Katlav, M. Machine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiber. Asian J. Civil Eng. 25 (6), 4541–4562. https://doi.org/10.1007/s42107-024-01064-2 (2024).

    Google Scholar 

  25. Soni, A. & Nateriya, R. Exploring Ultimate Flexural Strengths of Ultra-High-Performance Concrete (UHPC) Samples through Experimental Analysis in Comparison with Ordinary Concrete Structures, National Academy Science Letters, Nov. (2024). https://doi.org/10.1007/s40009-024-01520-2

  26. Lima, C. A. M., Coelho, A. L. V. & Von Zuben, F. J. Ensembles of support vector machines for regression problems, in Proceedings of the International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), 2381–2386., 2381–2386. (2002). https://doi.org/10.1109/IJCNN.2002.1007514

  27. Qian, Y., Sufian, M., Hakamy, A., Farouk Deifalla, A. & El-said, A. Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete. Front. Mater. 9 https://doi.org/10.3389/fmats.2022.1114510 (2023).

  28. Das, P. & Kashem, A. Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations. Case Stud. Constr. Mater. 20, e02723. https://doi.org/10.1016/j.cscm.2023.e02723 (2024).

    Google Scholar 

  29. Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20 (3), 273–297. https://doi.org/10.1007/BF00994018 (1995).

    Google Scholar 

  30. Prasad, D. V. V. & Jaganathan, S. Null-space based facial classifier using linear regression and discriminant analysis method. Cluster Comput. 22 (S4), 9397–9406. https://doi.org/10.1007/s10586-018-2178-z (2019).

    Google Scholar 

  31. Rasmussen, C. E. Gaussian Processes in Machine Learning, 63–71. (2004). https://doi.org/10.1007/978-3-540-28650-9_4

  32. Gad, A. F. Artificial Neural Networks. In Practical Computer Vision Applications Using Deep Learning with CNNs 45–106 (A, 2018). https://doi.org/10.1007/978-1-4842-4167-7_2.

    Google Scholar 

  33. Gayathri, R., Rani, S. U., Čepová, L., Rajesh, M. & Kalita, K. A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength, Processes, 10 (7), 1387, (2022). https://doi.org/10.3390/pr10071387

  34. Quinlan, J. R. Induction of decision trees. Mach. Learn. 1 (1), 81–106. https://doi.org/10.1007/BF00116251 (1986).

    Google Scholar 

  35. Ho, T. K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20 (8), 832–844. https://doi.org/10.1109/34.709601 (1998).

    Google Scholar 

  36. Chen, T. & Guestrin, C. XGBoost, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 785–794. (2016). https://doi.org/10.1145/2939672.2939785

  37. Zhang, Y., Wang, H., Liu, J., Liu, F. & Lv, X. Intelligent structural design of composite concrete-encased steel columns based on hybrid machine learning and multiobjective optimization. Structural Concrete Aug. https://doi.org/10.1002/suco.70292 (2025).

    Google Scholar 

  38. Demšar, J. No Title. J. Mach. Learn. Res. 7, 1–30. https://doi.org/10.5555/1248547.1248548 (2006).

    Google Scholar 

  39. Wei, J. et al. Transfer learning framework for the wind pressure prediction of high-rise building surfaces using wind tunnel experiments and machine learning. Build. Environ. 271, 112620. https://doi.org/10.1016/j.buildenv.2025.112620 (2025).

    Google Scholar 

  40. Zhang, Y. et al. Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO2 Emissions Trade-Offs. Buildings 15 (15), 2640. https://doi.org/10.3390/buildings15152640 (2025).

    Google Scholar 

  41. Wang, K. et al. Exploration of computational formulations for wind-induced interference effects on high-rise buildings via Kolmogorov–Arnold networks. Developments Built Environ. 24 (100770). https://doi.org/10.1016/j.dibe.2025.100770 (2025).

  42. Wang, K., Shen, T., Wei, J., Liu, J. & Hu, W. An intelligent framework for deriving formulas of aerodynamic forces between high-rise buildings under interference effects using symbolic regression algorithms. J. Building Eng. 99 (111614). https://doi.org/10.1016/j.jobe.2024.111614 (2025).

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/147/45. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2026-1161-04”. We would like to express my gratitude to GPT 4o for producing Figures 1, 2, and 3. We assure that these figures are only to show a few steps of the experiments and have no impact on the results and achievements of this paper.

Funding

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/147/45. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2025-1161-XX“.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi, UAE

    Manish Kewalramani

  2. Center of Research and Strategic Studies, Lebanese French University, Erbil, Iraq

    Arsalan Mahmoodzadeh

  3. Department of Civil Engineering, College of Engineering, King Khalid University, Kingdom of Saudi Arabia, PO Box 394, 61411, Abha, Saudi Arabia

    Mohamed Hechmi El Ouni

  4. Center for Engineering and Technology Innovations, King Khalid University, Abha, 61421, Saudi Arabia

    Mohamed Hechmi El Ouni

  5. Department of Civil Engineering, Faculty of Engineering, University of Tabuk, Tabuk, 47512, Saudi Arabia

    Abdulaziz Alghamdi

  6. Department of Civil Engineering, College of Engineering, Northern Border university, Arar, 73222, Saudi Arabia

    Anwar Ahmed

  7. Mechanical Engineering Department, College of Engineering at Al-Kharj, Prince Sattam Bin Abdulaziz University, Al Kharj, 16273, Saudi Arabia

    Ibrahim Albaijan

  8. Department of Mechanical Engineering, College of engineering, Addis Ababa Science and Technology University, Addis Ababa, Po Box 16417, Ethiopia

    Sivaprakasam Palani

  9. Civil Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia

    Raouf Hassan

Authors
  1. Manish Kewalramani
    View author publications

    Search author on:PubMed Google Scholar

  2. Arsalan Mahmoodzadeh
    View author publications

    Search author on:PubMed Google Scholar

  3. Mohamed Hechmi El Ouni
    View author publications

    Search author on:PubMed Google Scholar

  4. Abdulaziz Alghamdi
    View author publications

    Search author on:PubMed Google Scholar

  5. Anwar Ahmed
    View author publications

    Search author on:PubMed Google Scholar

  6. Ibrahim Albaijan
    View author publications

    Search author on:PubMed Google Scholar

  7. Sivaprakasam Palani
    View author publications

    Search author on:PubMed Google Scholar

  8. Raouf Hassan
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.K. and A.M. conceptualized the study and designed the research methodology. M.K., A.M., and M.H.E.O. carried out the experimental investigation and data curation. A.M. developed the machine learning models and performed the computational analysis. R.H. and S.P. contributed to the interpretation of results and provided critical technical insights related to fiber-reinforced ultra-high-performance concrete behavior at elevated temperatures. A.A. (Abdulaziz Alghamdi), A.N. (Anwar Ahmed), and I.A. supervised the research, provided resources, and critically reviewed the manuscript for intellectual content. M.K. and A.M. wrote the original draft of the manuscript. All authors reviewed, edited, and approved the final version of the manuscript.

Corresponding author

Correspondence to Sivaprakasam Palani.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kewalramani, M., Mahmoodzadeh, A., El Ouni, M.H. et al. Machine learning-based prediction of residual flexural strength in fiber-reinforced ultra-high-performance concrete under elevated temperatures. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43833-w

Download citation

  • Received: 19 December 2025

  • Accepted: 06 March 2026

  • Published: 23 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-43833-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Fiber-reinforced ultra-high-performance concrete
  • Residual flexural strength
  • Machine learning
  • SHAP analysis
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing